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import wandb |
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import click |
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import os |
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import sys |
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import pickle |
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import numpy as np |
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from PIL import Image |
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import glob |
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import torch |
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from configs import paths_config, hyperparameters, global_config |
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from IPython.display import display |
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import matplotlib.pyplot as plt |
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from scripts.latent_editor_wrapper import LatentEditorWrapper |
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image_dir_name = '/home/sayantan/processed_images' |
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use_multi_id_training = False |
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global_config.device = 'cuda' |
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paths_config.e4e = '/home/sayantan/PTI/pretrained_models/e4e_ffhq_encode.pt' |
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paths_config.input_data_id = image_dir_name |
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paths_config.input_data_path = f'{image_dir_name}' |
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paths_config.stylegan2_ada_ffhq = '/home/sayantan/PTI/pretrained_models/ffhq.pkl' |
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paths_config.checkpoints_dir = '/home/sayantan/PTI/' |
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paths_config.style_clip_pretrained_mappers = '/home/sayantan/PTI/pretrained_models' |
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hyperparameters.use_locality_regularization = False |
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hyperparameters.lpips_type = 'squeeze' |
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model_id = "MYJJDFVGATAT" |
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def display_alongside_source_image(images): |
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res = np.concatenate([np.array(image) for image in images], axis=1) |
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return Image.fromarray(res) |
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def load_generators(model_id, image_name): |
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with open(paths_config.stylegan2_ada_ffhq, 'rb') as f: |
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old_G = pickle.load(f)['G_ema'].cuda() |
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with open(f'{paths_config.checkpoints_dir}/model_{model_id}_{image_name}.pt', 'rb') as f_new: |
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new_G = torch.load(f_new).cuda() |
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return old_G, new_G |
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def plot_syn_images(syn_images,text): |
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for img in syn_images: |
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img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy()[0] |
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plt.axis('off') |
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resized_image = Image.fromarray(img,mode='RGB').resize((256,256)) |
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display(resized_image) |
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del img |
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del resized_image |
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torch.cuda.empty_cache() |
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def syn_images_wandb(img): |
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img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy()[0] |
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plt.axis('off') |
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resized_image = Image.fromarray(img,mode='RGB').resize((256,256)) |
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return resized_image |
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def edit(image_name): |
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generator_type = paths_config.multi_id_model_type if use_multi_id_training else image_name |
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old_G, new_G = load_generators(model_id, generator_type) |
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w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}' |
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embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}' |
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w_pivot = torch.load(f'{embedding_dir}/0.pt') |
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old_image = old_G.synthesis(w_pivot, noise_mode='const', force_fp32 = True) |
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new_image = new_G.synthesis(w_pivot, noise_mode='const', force_fp32 = True) |
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latent_editor = LatentEditorWrapper() |
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latents_after_edit = latent_editor.get_single_interface_gan_edits(w_pivot, [i for i in range(-5,5)]) |
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for direction, factor_and_edit in latents_after_edit.items(): |
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for editkey in factor_and_edit.keys(): |
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new_image = new_G.synthesis(factor_and_edit[editkey], noise_mode='const', force_fp32 = True) |
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image_pil = syn_images_wandb(new_image).save(f"/home/sayantan/PTI/{direction}/{editkey}/{image_name}.jpg") |
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if __name__ == '__main__': |
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for image_name in [f.split(".")[0].split("_")[2] for f in sorted(glob.glob("*.pt"))]: |
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edit(image_name) |
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